Top Senior Data Science Jobs in Minneapolis, MN

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Lead end-to-end generative AI and machine learning initiatives, designing scalable and secure AI architectures and developing predictive, prescriptive, optimization, NLP, deep learning, and multimodal models. Analyze healthcare datasets, conduct experiments and A/B tests, evaluate emerging approaches, optimize production systems, and translate findings into executive-level recommendations. The role also emphasizes responsible AI, including fairness, transparency, and accountability.

Leads Responsible AI data science initiatives by developing and validating AI testing protocols, automating analyses with Python, identifying PII and PHI vulnerabilities, and designing red-team scenarios for conversational AI. The role documents responsible AI practices, supports governance and regulatory compliance, communicates findings to legal, compliance, and technical stakeholders, and mentors colleagues. It requires expertise across traditional machine learning, generative AI, agentic techniques, AI ethics, fairness, and the full AI lifecycle.

Leads data science initiatives involving machine learning, predictive modeling, statistical techniques, and enterprise decisioning. Mentors data scientists, evaluates modeling approaches, partners with business and technology teams, and develops production-ready cloud and full-stack capabilities. The role also supports generative and agentic AI, CI/CD, containerization, observability, and scalable deployment of governed AI systems.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Lead design, development, and deployment of AI/ML models (forecasting, optimization, RL, generative AI) for pricing and promotion. Extract and integrate complex data, monitor model performance, establish AI engineering patterns, document code, and communicate results to technical and non-technical stakeholders to operationalize models across the supply chain.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Design and analyze rigorous messaging experiments using A/B testing and causal inference. Build measurement frameworks, data pipelines, dashboards, automated analyses, and scalable data solutions for messaging campaigns. Partner with CRM, Product, Algorithms, and Engineering teams to translate ambiguous business questions into actionable recommendations and experimentation standards. Support messaging strategy across channels, timing, content, targeting, and new business verticals.

Develop and productionize machine learning models and predictive analytics using Python, Databricks, PySpark, SQL, and Delta Lake. Build scalable data science workflows, resolve healthcare data quality and entity-resolution issues, and apply Master Data Management practices. Partner with engineering, product, analytics, and business teams to deliver governed, HIPAA-compliant solutions. Perform exploratory analysis, feature engineering, model evaluation, deployment, documentation, and data lineage management for healthcare and member engagement initiatives.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Develop scalable operations research and data science solutions using optimization, simulation, machine learning, and information retrieval. Partner with technical and business teams to frame problems, build production-ready analytical models, evaluate performance, recommend technical roadmaps, and deliver actionable insights. Lead data science practices, communicate recommendations, improve model quality, and establish long-term processes, frameworks, tools, and standards.

Develop marketing analytics and data science models to measure campaign effectiveness, ROI, attribution, customer lifetime value, and marketing impact. Apply causal inference, experimentation, and Marketing Mix Modeling; automate model pipelines and monitoring; build insight-ready datasets and self-service dashboards; and communicate actionable findings to marketing and executive stakeholders. The role also supports campaign analysis, data requirements, training, and strategic planning.